Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Why the next Log4Shell will be won or lost in the first 72 hours—and what a modern zero‑day workflow looks like. Every security team remembers where they were when Log4Shell dropped. A quiet Friday afternoon in December 2021 turned into a weekend of war rooms, emergency patches, and executive updates. Years on, the Log4j fallout still shows up in breach reports—a stubborn reminder that zero‑days don’t end when the news cycle does.

SAST, or Static Application Security Testing, is a method of analyzing source code to find vulnerabilities before the application is deployed. It's a type of white box testing that scans the code without executing it, looking for weaknesses that could be exploited. SAST helps developers identify and fix security issues early in the Software Development Life Cycle (SDLC), potentially reducing costs and improving the overall security posture of the application.

Software Composition Analysis (SCA) tools expose the risks in open source dependencies by identifying vulnerabilities, outdated dependencies, and license issues in your codebase. Top solutions include Mend.io (best for automated remediation and proactive SCA), Sonatype Lifecycle (known for enterprise policy management), Snyk (known for developer experience), and Checkmarx SCA (known for comprehensive coverage).

An AI just found critical vulnerabilities that survived decades of human review. If your security program isn’t built for this moment, it’s already behind. Surprise! An AI just did what your security team couldn’t.

Mend.io’s new Docker Hardened Images integration brings DHI intelligence directly into the AppSec workflow, giving a smarter, faster path to container security. Container scanning has a noise problem. Run a standard scan against any production image, and you’ll surface thousands of CVEs.

AI application security protects AI-powered apps, including those powered by large language models ( LLMs), from unique threats like prompt injection, data poisoning, and model theft. It achieves this by securing the entire lifecycle, including code, data, algorithms, and APIs, using specialized tools and processes that go beyond traditional security measures. It involves securing the AI model’s behavior, training data, and outputs.

On March 30-31, 2026, threat actors published two malicious versions of the popular HTTP library axios (versions 1.14.1 and 0.30.4) to the npm registry. Both versions included a new dependency named plain-crypto-js which, in its 4.2.1 release, contained a fully-featured cross-platform dropper that silently installed a Remote Access Trojan (RAT) on developer machines.

Part 1 covered CanisterWorm, the self-spreading npm worm. Part 2 covered the malicious LiteLLM package and its.pth persistence. This post covers the third wave: a compromised telnyxPyPI package that hides its payload inside audio files and delivers entirely different malware depending on the victim’s operating system.

Part 1 covered CanisterWorm, the self-spreading npm worm. This post covers the next wave: a malicious LiteLLM PyPI package carrying the most capable credential stealer TeamPCP has deployed yet. On March 24, 2026, two versions of litellm, one of the most widely used Python libraries for working with AI language model APIs, were published to PyPI carrying a hidden credential stealer. Versions 1.82.7 and 1.82.8 never appeared on the official LiteLLM GitHub repository.

On March 20, 2026 at 20:45 UTC, Aikido Security detected an unusual pattern across the npm registry: dozens of packages from multiple organizations were receiving unauthorized patch updates, all containing the same hidden malicious code. What they had caught was CanisterWorm, a self-spreading npm worm deployed by the threat actor group TeamPCP. We track this incident as MSC-2026-3271.